Quantum-like models are mathematical frameworks that apply quantum formalism to describe cognitive or information processes beyond quantum physics.Quantum-like models transfer the mathematical apparatus of quantum mechanics (e.g., Hilbert spaces—abstract vector spaces for states, operators—ways of measurement, probability amplitudes—quantities whose squared modulus gives probability) to the description of cognitive processes, social interactions, or biological systems. They do not claim that the brain is literally quantum but use a convenient formalism to account for contextuality, inconsistency, and question order.
Pioneering work in this area was done by mathematicians and cognitive psychologists such as D. Aerts and A. Khrennikov starting in the 1990s. They showed that many 'irrational' human decisions are well described by quantum-like probabilities. Later, the field was taken up by quantum information specialists. David Deutsch contributed to quantum computing, providing tools; John Archibald Wheeler reflected on the participation of the observer, which resonates with quantum-like approaches.
The state of an agent is described by a vector in Hilbert space (a complex vector space with an inner product). Choices are represented by projection operators. The probability of an outcome is given by the squared modulus of the projection (Born rule). Context influences like a change of basis. This allows quantum-like effects: interference (when the sum of probabilities does not reduce to the classical sum) and violation of distributivity. The principle of superposition and interference of states is applied.
Helps create more flexible AI algorithms that can handle contradictory data.
These models have explained decision-making paradoxes, such as violations of classical probability laws in surveys.















